Technical and Environmental Sustainability of Pharmaceutical Wastewater Treatment Using Ce-NaY Zeolite-Modified Polyethersulfone (PES) Membranes: A Life Cycle Assessment Approach
Bibliographic record
Abstract
This study investigated incorporating Ce-NaY zeolite into poly(ether sulfone) (PES) membranes to enhance wastewater treatment performance and reduce environmental impact. PES membranes, while strong and chemically stable, suffer from fouling and limited permeability. Modified membranes showed improved thermal stability (degradation at 350 °C) and a porous structure, especially at lower zeolite concentrations. The PES-Ce0.6 membrane achieved a water flux of 86 L/m 2 ·h at 5 bar, significantly higher than the unmodified PES membrane’s 39 L/m 2 .h. Antifouling tests revealed a 92% flux recovery ratio (FRR) for PES-Ce0.6. Pharmaceutical rejection tests showed up to 97% removal of various pharmaceuticals, including approximately 94% removal of amoxicillin (100 ppm), and high removal rates for penicillin G, levofloxacin, famotidine, ranitidine, and cefamezin. Life cycle assessment (LCA) demonstrated significant environmental benefits for the modified membranes, including reductions in human health damage (up to 48.95%), ecosystem damage (up to 23.97%), and resource depletion (up to 47.66%), with a 34.46% total weight improvement for the PES-Ce0.6 variant. These results indicate that Ce-NaY zeolite modification enhances PES membranes’ performance and sustainability for water purification and pharmaceutical removal.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".